Large-Scale Shill Bidder Detection in E-commerce

Michael Fire; Rami Puzis; Dima Kagan; Yuval Elovici

doi:10.1145/3589462.3589479

Large-Scale Shill Bidder Detection in E-commerce

Michael Fire, Rami Puzis, Dima Kagan, Yuval Elovici

Department of Software and Information Systems Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

User feedback is one of the most effective methods to build and maintain trust in electronic commerce platforms. Unfortunately, dishonest sellers often bend over backward to manipulate users' feedback or place phony bids in order to increase their own sales and harm competitors. The black market of user feedback, supported by a plethora of shill bidders, prospers on top of legitimate electronic commerce. In this paper, we investigate the ecosystem of shill bidders based on large-scale data by analyzing hundreds of millions of users who performed billions of transactions, and we propose a machine-learning-based method for identifying communities of users that methodically provide dishonest feedback. Our results show that (1) shill bidders can be identified with high precision based on their transaction and feedback statistics; and (2) in contrast to legitimate buyers and sellers, shill bidders form cliques to support each other.

Original language	English
Title of host publication	27th International Database Engineered Applications Symposium, IDEAS 2023
Editors	Richard Chbeir, Mirjana Ivanovic, Yannis Manolopoulos, Peter Z. Revesz
Publisher	Association for Computing Machinery
Pages	79-86
Number of pages	8
ISBN (Electronic)	9798400707445
DOIs	https://doi.org/10.1145/3589462.3589479
State	Published - 26 May 2023
Event	27th International Database Engineered Applications Symposium, IDEAS 2023 - Heraklion, Greece Duration: 5 May 2023 → 7 May 2023

Publication series

Name	ACM International Conference Proceeding Series

Conference

Conference	27th International Database Engineered Applications Symposium, IDEAS 2023
Country/Territory	Greece
City	Heraklion
Period	5/05/23 → 7/05/23

Keywords

Big Data
Cyber Security & Privacy
Data Science
Ecommerce
Fraud Detection
Social Network Analysis

ASJC Scopus subject areas

Human-Computer Interaction
Computer Networks and Communications
Computer Vision and Pattern Recognition
Software

Access to Document

10.1145/3589462.3589479

Cite this

Fire, M., Puzis, R., Kagan, D., & Elovici, Y. (2023). Large-Scale Shill Bidder Detection in E-commerce. In R. Chbeir, M. Ivanovic, Y. Manolopoulos, & P. Z. Revesz (Eds.), 27th International Database Engineered Applications Symposium, IDEAS 2023 (pp. 79-86). (ACM International Conference Proceeding Series). Association for Computing Machinery. https://doi.org/10.1145/3589462.3589479

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title = "Large-Scale Shill Bidder Detection in E-commerce",

abstract = "User feedback is one of the most effective methods to build and maintain trust in electronic commerce platforms. Unfortunately, dishonest sellers often bend over backward to manipulate users' feedback or place phony bids in order to increase their own sales and harm competitors. The black market of user feedback, supported by a plethora of shill bidders, prospers on top of legitimate electronic commerce. In this paper, we investigate the ecosystem of shill bidders based on large-scale data by analyzing hundreds of millions of users who performed billions of transactions, and we propose a machine-learning-based method for identifying communities of users that methodically provide dishonest feedback. Our results show that (1) shill bidders can be identified with high precision based on their transaction and feedback statistics; and (2) in contrast to legitimate buyers and sellers, shill bidders form cliques to support each other.",

keywords = "Big Data, Cyber Security & Privacy, Data Science, Ecommerce, Fraud Detection, Social Network Analysis",

author = "Michael Fire and Rami Puzis and Dima Kagan and Yuval Elovici",

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Fire, M , Puzis, R, Kagan, D & Elovici, Y 2023, Large-Scale Shill Bidder Detection in E-commerce. in R Chbeir, M Ivanovic, Y Manolopoulos & PZ Revesz (eds), 27th International Database Engineered Applications Symposium, IDEAS 2023. ACM International Conference Proceeding Series, Association for Computing Machinery, pp. 79-86, 27th International Database Engineered Applications Symposium, IDEAS 2023, Heraklion, Greece, 5/05/23. https://doi.org/10.1145/3589462.3589479

Large-Scale Shill Bidder Detection in E-commerce. / Fire, Michael ; Puzis, Rami; Kagan, Dima et al.
27th International Database Engineered Applications Symposium, IDEAS 2023. ed. / Richard Chbeir; Mirjana Ivanovic; Yannis Manolopoulos; Peter Z. Revesz. Association for Computing Machinery, 2023. p. 79-86 (ACM International Conference Proceeding Series).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Elovici, Yuval

PY - 2023/5/26

Y1 - 2023/5/26

N2 - User feedback is one of the most effective methods to build and maintain trust in electronic commerce platforms. Unfortunately, dishonest sellers often bend over backward to manipulate users' feedback or place phony bids in order to increase their own sales and harm competitors. The black market of user feedback, supported by a plethora of shill bidders, prospers on top of legitimate electronic commerce. In this paper, we investigate the ecosystem of shill bidders based on large-scale data by analyzing hundreds of millions of users who performed billions of transactions, and we propose a machine-learning-based method for identifying communities of users that methodically provide dishonest feedback. Our results show that (1) shill bidders can be identified with high precision based on their transaction and feedback statistics; and (2) in contrast to legitimate buyers and sellers, shill bidders form cliques to support each other.

AB - User feedback is one of the most effective methods to build and maintain trust in electronic commerce platforms. Unfortunately, dishonest sellers often bend over backward to manipulate users' feedback or place phony bids in order to increase their own sales and harm competitors. The black market of user feedback, supported by a plethora of shill bidders, prospers on top of legitimate electronic commerce. In this paper, we investigate the ecosystem of shill bidders based on large-scale data by analyzing hundreds of millions of users who performed billions of transactions, and we propose a machine-learning-based method for identifying communities of users that methodically provide dishonest feedback. Our results show that (1) shill bidders can be identified with high precision based on their transaction and feedback statistics; and (2) in contrast to legitimate buyers and sellers, shill bidders form cliques to support each other.

KW - Big Data

KW - Cyber Security & Privacy

KW - Data Science

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ER -

Large-Scale Shill Bidder Detection in E-commerce

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

Access to Document

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